Project Summary Currently, the most promising approach to control the HIV epidemic is to prevent new infections. To this end, early detection of new infections and monitoring the growth of the epidemic are critical. We propose statistical measurement techniques to identify acutely infected individuals;to simultaneously measure comorbidities;to improve the efficacy of treatment programs by monitoring the emergence of drug resistance;and to evaluate prevention program performance and monitor the epidemic. In summary, we present extensions of matrix pooling to identify acutely infected individuals, to identify individ- uals with drug resistant mutations and to estimate incidence of disease. We propose cluster detection methods to identify outbreaks of new infections, individuals impacted by comorbidities and relationships between HIV and comorbidities. We investigate several methods to estimate parameters associated with HIV epidemic monitoring, including mathematical modeling to estimate the reproductive number, viral load averaging, and annealing esti- mators. We introduce methods for investigating the order of appearance of TB and HIV resistance mutations with a goal of developing optimal sequences of anti-TB therapies. We also present extensions of lot quality assurance sampling (LQAS), including Large Country-LQAS and combining pooling with LQAS to classify areas with high burden of comorbidities.